A Type I error occurs when a researcher rejects the null hypothesis when it is actually true. Essentially, it is a false positive, meaning that the researcher concludes that there is an effect or a difference when in reality, none exists. A type II error occurs when a researcher fails to reject the null hypothesis when it is actually false. This is a false negative, meaning that the researcher concludes that there is no effect or difference when there actually is one (Cozby& Bates, 2024). Jim Frost (n.d.) provides a good analogy: the fire alarm rings when there is a fire and does not ring in the absence of a fire. However, if the alarm rings when there is no fire, it is a false positive, or a Type I error in statistical terms. Conversely, if the fire alarm fails to ring when there is a fire, it is a false negative or a Type II error. Another example of a Type I error would be concluding a drug lowers blood pressure when it doesn't. An example of a Type II error would be concluding a drug doesn't lower blood pressure when it actually does. A Type I error is a false positive, and a Type II error is a false negative.This was a good explanation of Type 1 and Type 2. You also gave great examples to better understand the definitions of both. These terms can easily be switched up because of how similar they are. When doing researcher everyone wants it to be successful to get the data they need and to better understand the research. When a Type 2 error occurs how could someone recove